What Dance Scholars can Learn from Warehouse Surveillance: Emic Approaches to Temporal Action Segmentation
Co-sponsored by the Berkeley Institute for Data Science and the School of Information
Intangible cultural heritage (ICH) presents unique challenges for scholarly analysis. Unlike material heritage with its monuments and artifacts, ICH exists primarily in performance, practice, and living tradition. Video recordings offer some of the most comprehensive documentation of these ephemeral expressions, yet analyzing large video archives has remained a daunting challenge.
In this talk, I will describe how temporal action segmentation (TAS), a computational approach developed for activity recognition, can be adapted to ICH video analysis, enabling researchers to identify culturally meaningful performance segments at scale. Through a case study of Javanese puppet theater comprising over 10,000 recordings, I will demonstrate how TAS can address longstanding questions about performance evolution while respecting culture-specific narrative structures.
This approach advances what I term emic segmentation, the computational study of semantic units as they are conceived and valued within their originating cultural contexts. I will also present open-source tools and a complete methodological pipeline to extend this work to other cultural contexts.
Speaker
Miguel Escobar Varela
Miguel Escobar Varela is associate professor at the Department of English, Linguistics and Theatre Studies and deputy director of the Centre for Computational Social Science and Humanities at the National University of Singapore. In his research, he studies the changing landscape of Southeast Asian cultural heritage by combining fieldwork with computational methods (such as natural language processing, computer vision and network analysis). He is the author of Theatre as Data (University of Michigan Press, 2021). His papers, datasets and research software are available at https://miguelescobar.com.
